Matthias Linus Kellner

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Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or the ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach ...

Knowledge of the mechanism by which polymorphic inorganic species, such as carbonates, are formed is crucial to understand and guide the selective crystallization of end products. Recently it has been shown that a key step in the crystallization of calcium ...

Matthias Linus Kellner

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Uncertainty quantification by direct propagation of shallow ensembles

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Prediction rigidities for data-driven chemistry

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR